Abstract:

The purpose of this paper is to find out if there is a link between Myers-Briggs Type Indicator (MBTI) personality traits and how well computer science students do in a programming class. The MBTI test categorizes participants into one of 16 groups based on their personality traits such as extraversion or introversion, intuition or sensing, thinking or feeling, and judging or perceiving. It was inspired by Carl Jung's personality theory, developed in the 1920s, and has been present in both popular psychology and academic research ever since. The success of famous people has been attributed to their personalities, and MBTI tests are even used at job interviews for certain positions because of the belief that one's personality can predict one's success in a given field. This paper looks at the grades 31 students got in a computer programming course and which personality trait they belong to. All students had their progress tracked throughout the whole study year after which every student underwent a personality trait test that revealed each student’s MBTI trait. We use this trait as an input in our research, together with the student scores, attendance records and completed activities during the course, interested in finding out if any combination of traits has a strong effect on how engaged and successful students are, as well as what kind of relationship these traits have with each student's attendance record. The findings show that there are traits that are beneficial to computer science students and traits that can be detrimental to the success of these students in computer programming courses. Students with both judging and thinking traits (INTJ, ENTJ, ISTJ, ESTJ) do better on programming challenges and tasks. Over the years, it has been suggested that introverts make better computer programmers, but the current study didn’t reveal any obvious advantage for them over extroverts. The results suggest that a student's personality can be used as a predictor of their success, at least in computer programming courses. When combined with other factors, like cognitive skills or field-related experience, which have proven to be useful input for different machine learning techniques and algorithms, the predictive power of personality traits is further increased.

Keywords:

Personality traits, programming skills, Myers-Briggs Type indicator, student performance.